class_docstr: Abstract baseclass of an optimizer to be used with ES.
docstr: Compute a single step, to be implemented by the concrete optimizers
docstr_tok: ["Compute","a","single","step",",","to","be","implemented","by","the","concrete","optimizers"]
code: def _compute_step(self, global_gradient: np.ndarray) -> np.ndarray:
      """Compute a single step, to be implemented by the concrete optimizers"""
      raise NotImplementedError
  
file_path: maze/maze/train/trainers/es/optimizers/base_optimizer.py
imports: from abc import ABC, abstractmethod
  from typing import Optional
  import numpy
  from maze.core.agent.torch_model import TorchModel
  from maze.train.trainers.es.es_utils import get_flat_parameters, set_flat_parameters
label_desc: Use image node_id 4 for calling the Optimizer obj's underlying member method code with example usage: obj._compute_step(global_gradient) without return types